Search Results (217)

As one of the three major bays in the Chinese Bohai Sea, Bohai Bay is located in a semi-encircled area consisting of three important provinces and cities with rich energy and fishery resources. The bay is not only a maritime gateway and transportation hub but also an important industrial base, energy production base, and port. In this study, we combined Landsat remote sensing and Geographic Information System technologies to extract the coastline of Bohai Bay from 2001 to 2021 and obtained the variation in coastline length by refinement vector processing. Sediment as the natural driver was quantitatively analyzed based on sand transport in the Yellow River and Hai River. Moreover, port construction was qualitatively analyzed as the anthropogenic driver. The results demonstrated that the coastline of Bohai Bay showed an overall growth trend in this period, with a total increase of 881.05 km in shoreline length; the main increase was in the artificial shoreline. The two natural driving factors, sediment and hydrodynamic conditions, were weak, and the anthropogenic driving factor, i.e., various human activities, played a dominant role in the variation in the Bohai Bay shoreline in the past 20 years. The extracted shoreline information is important not only for the rational and effective development and utilization of the various natural resources in the coastal zone of Bohai Bay but also for the plan to develop this important region in the future. Full article

Heavy metal contamination in soil and the resulting groundwater pollution are common at many brownfield sites. Soil washing, which dissolves contaminants into a washing solution to separate them from the soil matrix, has emerged as a promising remediation strategy. This study assessed the feasibility of applying soil washing to Pb-contaminated soil collected from an industrial area within the Trieste Port Authority (Italy) through a series of leaching tests. Batch tests were conducted using ethylenediaminetetraacetic acid (EDTA)-based extractants combined with various reducing agents to identify the most effective and environmentally sustainable washing solution. The results show that coupling EDTA with hydroxylamine hydrochloride or sodium dithionite significantly enhanced Pb solubilisation compared with EDTA alone, with dithionite emerging as the most suitable reducing agent due to its lower toxicity and reduced environmental impact. Sequential extraction tests revealed that up to 50% of total Pb could be removed after repeated washing cycles. Column leaching tests further confirmed the high efficiency of the EDTA–sodium dithionite system, achieving Pb removal rates of approximately 70% under continuous flow conditions. Overall, the results demonstrate that EDTA combined with low-dose sodium dithionite provides an effective and practical remediation strategy for heavily polluted industrial soils. Full article

As a form of living heritage, Historic Urban Landscapes (HULs) have long been limited by the static perspectives and reductionist tendencies of conventional conservation and research approaches. Although the geological and archaeological concept of “stratification” offers a methodological basis for understanding the diachronic evolution of heritage, its unidimensional temporal lens fails to capture the inherent complexity and systemic nature of historic urban landscapes. To address this gap, this study proposes a “multidimensional stratification” theoretical framework through theoretical critique and paradigm reconstruction. The framework introduces innovations at the ontological, epistemological, and methodological levels, positing that the evolution of historic urban landscapes emerges from the nonlinear interaction and dynamic interweaving of four core dimensions: time, space, society, and value. It further systematizes five intrinsic attributes of such landscapes: authenticity, integrity, continuity, adaptability, and dynamism. Building on this foundation, the paper constructs a systematic analytical pathway—elements–processes–patterns–modes–drivers–characteristics—that enables dynamic analysis from micro-level identification to macro-level generalization, offering a scalable tool for HUL conservation and regeneration. To demonstrate the framework’s applicability, the historic urban area of Shenyang—a nationally designated historical and cultural city—is selected as a case study. Its urban landscape comprises four core districts: the Shengjing City District, the South Manchuria Railway Concession District, the Commercial Port District, and the Tiexi Industrial District, representing historical strata from the Qing dynasty capital, modern colonial planning, commercial opening, to industrial heritage. Using the multidimensional stratification approach, this study elucidates the spatial complexity, temporal nonlinearity, social dynamism, and value pluralism embedded in Shenyang’s historic urban area. Corresponding conservation strategies grounded in holism, dynamism, and differentiation are proposed. The research not only advances the theoretical understanding of HUL but also provides a novel paradigm—integrating holistic, dynamic, and operational perspectives—for the conservation, renewal, and regenerative practice of historic urban landscapes worldwide. Full article

To evaluate the use of the magnetic susceptibility (MS) method as a rapid screening tool for metal enrichment in the karst estuarine systems, a case study was conducted in the Krka River Estuary (Croatia). Magnetic measurements were performed on 36 surface sediment samples collected along the entire estuary, as well as in 6 sediment cores taken from areas with different sedimentological properties and/or varying levels of anthropogenic pressure. The efficacy of MS as a proxy for metal enrichment was evaluated by correlating the obtained datasets with previously published metal concentrations in the same samples, utilising statistical methods to quantify these relationships. Susceptibility values in the Krka River Estuary are generally low (mean value 0.123 × 10⁻³ SI), reflecting a carbonate-dominated background typical of uncontaminated karst systems, but various local anomalies (max value 0.799 × 10⁻³ SI) coincide with areas of metal enrichment previously linked to industrial and port activities. Correlation and multivariate analyses show that, despite a low overall magnetic signal, elevated MS can successfully detect hotspots with increased metal levels (e.g., Pb, Mn), confirming that with careful calibration to local magnetic background values, this method provides an effective and analytically simple proxy for metal-enriched sediments in sensitive karst estuaries. Full article

To address ship-sourced water pollutant issues resulting from shipping industry growth and achieve precise supervision and effective management in coastal ports, this study develops a method for calculating and predicting the generation volume of oily sewage, domestic sewage and solid waste based on Automatic Identification System (AIS) data. First, a questionnaire survey (“Survey on Ship Water Pollutants”) is designed and implemented. Through analysis of questionnaire data, the ranges of values for the generation of oily sewage, domestic sewage, and solid waste from different ship types at China’s coastal ports are established. Additionally, onboard sampling is conducted to determine average emission factors for domestic sewage and oily sewage from typical ship types. Second, ship activities are derived from AIS data and combined with the established generation volume ranges for spatiotemporal calculation. Finally, a ConvLSTM (Convolutional Long Short-Term Memory) model is developed to predict the generation volume of water pollutant based on their spatiotemporal characteristics. Taking a major Chinese port area as a case study, the results indicate that pollutant generation volumes are significant in coastal port zones and main navigation channels, particularly between 15:00 and 16:00. chemical oxygen demand (COD), suspended solids (SS), and 5-day biochemical oxygen demand (BOD₅) levels in domestic sewage exceeded China’s national regulatory limits by 0.35 times, 2.88 times and 1.07 times, respectively, which can easily lead to a decrease in dissolved oxygen content in the water, affecting the respiration and survival of aquatic organisms. Petroleum content in oily sewage remained below the standard threshold. For pollutant generation volume prediction, the proposed ConvLSTM model achieved MAE and RMSE values of 0.0824 and 0.1433, respectively, outperforming other prediction models such as LSTM and CNN-LSTM. This research provides technical support for the prevention and control of water pollution from ships in coastal ports. The proposed AIS-driven framework and ConvLSTM prediction method are transferable and globally applicable, offering a reference for the environmental sustainability of port ecosystems, the global maritime pollution prevention, and the sustainable development of the shipping industry worldwide. Full article

The rapid diffusion of industry 4.0 technologies has substantially transformed the maritime transportation sectors by enabling data-driven operations, enhanced connectivity, and more intelligent decision-making processes. Digital technologies such as the Internet of Things (IoT), simulation systems, and advanced data analytics are increasingly reshaping operational structures in maritime logistics, positioning technological transformation as a strategic priority for firms. However, the weighting and prioritization of components emerging with industry 4.0 technologies remain an underexplored area in the literature. The primary motivation of this study is to determine the weights of these industry 4.0 components using the Bayesian Best Worst Method (BWM) and to reveal their corresponding credal ranking levels. In this context, the present study aims to evaluate and prioritize the critical industry 4.0 components influencing technological transformation processes using the Bayesian BWM. Bayesian BWM is preferred over alternative Multi Criteria Decision Making (MCDM) approaches due to its ability to explicitly model uncertainty within a probabilistic framework, generate more consistent weighting results, and flexibly incorporate decision-makers’ judgments. The findings reveal that safety and security (0.2945) constitute the most influential main component, underscoring the necessity of robust digital infrastructures and reliable systems within highly digitalized operational environments. Among the sub-components, data privacy (0.1301) demonstrates the highest global weight, highlighting the growing importance of safeguarding sensitive information in data-intensive digital systems. The results further indicate that autonomous operation and coordination play significant roles in facilitating efficient digital operations, particularly through real-time equipment monitoring and IoT-based operational visibility. Moreover, sustainability (0.1968) emerges as the second most important component, suggesting that organizations increasingly assess technological investments not only in terms of operational efficiency but also with respect to long-term resilience. Within this dimension, continuous training (0.0614) is identified as the most influential component, indicating that the success of digital transformation depends not only on technological infrastructure but also on the development of human capabilities. With the increasing digitalization of the maritime industry, protection against cyber threats has become essential for ensuring operational continuity and safeguarding data integrity. In this regard, adopting proactive cybersecurity strategies and continuously monitoring and updating systems are of critical importance. In the digital transformation of maritime transportation, integrating sustainability considerations is essential to ensure long-term operational efficiency and environmental responsibility. These practical implications are particularly relevant for policymakers, port authorities, and shipping companies seeking to enhance both digital capabilities and sustainable performance. Full article

Disaster risk in port and municipal environments increasingly emerges from the interaction between natural hazards, critical infrastructure exposure, and governance complexity. Although formal risk assessment frameworks are established, challenges remain in translating static hazard analyses into dynamic situational awareness during rapidly evolving events. This study presents a comparative analysis of four reference areas in the Adriatic–Ionian region—Shkodra (Albania), Pescolanciano (Italy), the Port of Bar (Montenegro), and the Port of Taranto (Italy)—to identify vulnerabilities and monitoring gaps in disaster prevention systems. Based on document analysis and cross-case synthesis, the findings distinguish environmentally driven municipal risks from hybrid industrial–logistical risk profiles in port environments. The results indicate that regulatory frameworks are in place, yet constraints persist in obtaining high-resolution, near-real-time spatial information during flood, landslide, wildfire, and industrial scenarios. This study assesses UAV-based monitoring as a complementary tool to enhance situational awareness within existing governance structures, contributing to improved integration between risk assessment and operational disaster prevention. Full article

The liner shipping industry is thriving in the low-carbon transition, and optimizing operational strategies for liquefied natural gas (LNG) dual-fuel ships has become a research hotspot. This research examines the impacts of the carbon tax, emission control area (ECA) policies, fuel price discounts and methane slip rate on fuel management strategies. Firstly, to reduce liner operating costs and adhere to ECA policies, this study develops a basic optimization model. Further, the model is extended to take into account the impact of fuel price discounts. Secondly, by linearizing multiple nonlinear terms, the operational strategies are obtained. Thirdly, taking a real vessel sailing between the Far East and Northwest Europe as a case study, this study identifies the ports for LNG and very low sulfur fuel oil (VLSFO) bunkering, determines the bunkering amounts and calculates the planned speeds. Furthermore, sensitivity analyses are conducted on fuel price difference, carbon tax rate and methane slip rate. Results show that fuel price difference, carbon tax rate, methane slip rate and fuel price discount exert a significant impact on ship operational decisions. To ensure the effectiveness of maritime decarbonization regulations, authorities should monitor ship engines with high methane slip rates. This study offers important references for shipping enterprises to meet ship emission policies and simultaneously cut operational costs. Full article

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

Arsenic (As) and mercury (Hg) are trace elements of major environmental and public health concern. Their relevance is due to their well-documented toxicological effects. In rapidly urbanizing port-industrial cities, soil contamination by these elements represents a critical challenge. This situation compromises sustainable urban development and environmental governance. This study had three main objectives: First, to evaluate the contamination status of As and Hg in urban soils using multiple geochemical indices; Second, to assess the potential human health risks associated with exposure in the urban environment of Talcahuano; Third, to identify the relative contributions of geogenic and anthropogenic sources based on spatial distribution patterns. A total of 420 soil samples were collected. These included 140 topsoil samples (TS; 0–10 cm), 140 subsoil samples (SS; 10–20 cm), and 140 deep-soil samples (DS; 150 cm). Arsenic concentrations were determined using hydride-generation atomic absorption spectrometry (HG-AAS). Mercury concentrations were measured by cold-vapour atomic absorption spectrometry (CV-AAS). Median As concentrations were 2.7 mg kg⁻¹ in TS, 3.1 mg kg⁻¹ in SS, and 2.5 mg kg⁻¹ in DS. The corresponding median Hg concentrations were 0.2 mg kg⁻¹ in TS and 1.4 mg kg⁻¹ in both SS and DS. Spatial distribution maps were generated through ordinary kriging interpolation. Geochemical baseline values were calculated using the median + 2 × MAD approach. The resulting baseline values were 7.8 mg kg⁻¹ for As and 3.6 mg kg⁻¹ for Hg. Contamination assessment was conducted using the geoaccumulation index (Igeo), enrichment factor (EF), and contamination factor (Cf). Results indicate that most soils are classified as uncontaminated. Enrichment levels were minimal and contamination factors were low. Nevertheless, isolated outliers were identified. These included one significantly enriched As sample and several moderately enriched or slightly contaminated Hg samples. Human health risk assessment incorporated the Hazard Index (HI) and Total Carcinogenic Risk (TCR). Results indicate that neither non-carcinogenic nor carcinogenic risks exceed acceptable thresholds at any investigated soil depth. Spatial analysis suggests that anthropogenic activities are the dominant sources of As and Hg in the study area. Traffic emissions and industrial activities appear to be the primary contributors. Full article

The large-scale dredging activities in port areas generate substantial quantities of dredged soil, leading to land occupation and disposal challenges, while industrial wastes such as fly ash and desulfurization gypsum remain underutilized. In this study, industrial wastes were employed as a curing agent to stabilize dredged soil, aiming to achieve both mechanical performance improvement and cost-effective recycling. In total, 100 g of curing agent was added to 1 kg of sludge. The optimal strength-maximizing formulation comprised 4.5% activator 1 #, 4.5% fly ash, 4.5% mineral powder, and 0.5% desulfurization gypsum. It achieved an unconfined compressive strength of 0.794 MPa. For enhanced cost-effectiveness, a modified binder blend (1.88% activator 1 #, 4.5% fly ash, 4.5% mineral powder, and 0.5% desulfurization gypsum) delivered 0.63 MPa at 28 days, satisfying mechanical construction specifications. Results demonstrate that unconfined compressive strength increases with solid wastes; however, with the extension of solidification time, the unconfined compressive strength of dredged soil gradually slows down. Full article

In China, ports have long served as a key engine of growth for coastal cities. Increases in coastal port throughput inevitably lead to port spatial expansion, which in turn drives construction land expansion in port cities. Consequently, ports are a critical factor shaping construction land expansion in coastal cities, with direct implications for spatial planning and sustainable development in coastal port cities. Therefore, it is necessary to examine how ports influence construction land expansion in coastal cities. This paper using multiple linear regression and binary logistic regression models and incorporating landscape metrics explores the impacts of ports on the expansion of urban construction land in coastal port cities. The findings reveal distinct characteristics of land expansion in port cities compared to non-port cities: (1) Macro-level changes: The expansion of construction land is driven by industrial restructuring, real estate development, port cargo traffic, population growth, and GDP growth. Industrial restructuring is the primary driver, while real estate development plays a significant role in land expansion. Port cargo demand serves as a unique driving factor compared to non-port cities, whereas population and GDP growth have minimal effects. (2) Micro-level spatial expansion: Land expansion is influenced by proximity to port shorelines, transportation infrastructure, and the degree of base construction land expansion. Expansion tends to concentrate along the port shoreline, transport hubs, and established urban areas. Elevation and slope are significant factors for coastal port cities, while rivers and proximity to core urban areas predominantly impact estuarine port cities. (3) Temporal patterns of expansion: Port development follows a phased pattern of land expansion: “Decline → Increase → Decline”. Ports also influence landscape patterns, with increased distance from the port shoreline leading to decreased patch density and higher landscape fragmentation. The results of this paper help to address gaps in existing research on how ports shape the spatial expansion of coastal cities. Furthermore, this paper provides insights for effective land use strategies, spatial planning, and port-city management, promoting coordinated land and marine development. It offers a foundation for addressing the integration of land and sea spatial planning in the “One Map” initiative. Full article

This study establishes a municipal-level evaluation system for rural revitalization in China, grounded in the five-sphere integrated framework encompassing “prosperous industries, livable ecology, civilized rural customs, effective governance, and affluent life.” Employing methodologies including the entropy weight-coupling coordination model, LISA spatiotemporal analysis, and multi-scale geographically weighted regression (MGWR), it empirically investigates the evolution and driving mechanisms of rural revitalization development across 282 prefecture-level cities from 2011 to 2023. The findings reveal: (1) Nationwide and regional rural revitalization levels demonstrate a consistent upward trajectory, progressing from a state of “Mild Disorder” to being “On the Verge of Disorder,” with a distinct gradient pattern of “Eastern Region > National Average > Central Region > Western Region.” (2) Significant global spatial correlation is observed, manifesting as polarization typified by “high–high” and “low–low” agglomeration, alongside notable volatility in Northeast and Southwest China. (3) Influencing factors display marked spatiotemporal heterogeneity. Agricultural production efficiency (North China) and technological innovation (nationwide, except the Yangtze River Delta) significantly foster rural revitalization. Conversely, economic development level (Northeast, Central, and Western China), government intervention (Northeast China), and industrial structure upgrading (Northwest China) exhibit constraining effects. The localized positive impacts of urbanization (border areas of Yunnan, Heilongjiang, Sichuan, Jilin, and Tibet) and opening up (border ports) are increasingly evident. Building on these insights, the study proposes recommendations—such as implementing differentiated regional policies, innovating spatial governance models, and activating multidimensional drivers—to overcome the “low-level lock-in” predicament and advance comprehensive rural revitalization. Furthermore, this paper reveals the patterns of multidimensional system coupling and the spatial heterogeneity of driving mechanisms. These findings provide a reference for deepening the understanding of geographical complexity within global sustainable development theory. Full article

Knowledge about vessel activity in port areas and around major industrial zones provides insights into economic trends, supports decision-making for shipping and port operators, and contributes to maritime safety. Vessel data from terrestrial receivers of the Automatic Identification System (AIS) have become increasingly openly available, and we demonstrate that such data can be used to infer port activities at high resolution and with precision comparable to official statistics. We analyze open-access AIS data from a three-month period in 2024 for Tokyo Bay, located in Japan’s most densely populated urban region. Accounting for uneven data coverage, we reconstruct vessel activity in Tokyo Bay at ~30 m resolution and identify 161 active berths across seven major port areas in the bay. During the analysis period, we find an average of $35\pm {17}_{\mathrm{stat}}$ vessels moving within the bay at any given time, and $293\pm {22}_{\mathrm{stat}}+{65}_{\mathrm{syst}}-{10}_{\mathrm{syst}}$ vessels entering or leaving the bay daily, with an average gross tonnage of ${\mathrm{11,860}}_{-50}^{+280}$. These figures indicate an accelerating long-term trend toward fewer but larger vessels in Tokyo Bay’s commercial traffic. Furthermore, we find that in dense urban environments, radio shadows in vessel AIS data can reveal the precise locations of inherently passive receiver stations. Full article

With the rapid development of industries such as construction and port hoisting, the operational safety of truck cranes in crowded areas has become a critical issue. Under complex working conditions, traditional monitoring methods are often plagued by issues such as compromised image quality, increased parallax computation errors, delayed fence response times, and inadequate accuracy in dynamic target recognition. To address these challenges, this study proposes a personnel intrusion detection system based on multimodal sensor fusion and dynamic prediction. The system utilizes the combined application of a binocular camera and a lidar, integrates the spatiotemporal attention mechanism and an improved LSTM network to predict the movement trajectory of the crane boom in real time, and generates a dynamic 3D fence with an advance margin. It classifies intrusion risks by matching the spatiotemporal prediction of pedestrian trajectories with the fence boundaries, and finally generates early warning information. The experimental results show that this method can significantly improve the detection accuracy of personnel intrusion under complex environments such as rain, fog, and strong light. This system provides a feasible solution for the safety monitoring of truck crane operations and significantly enhances operational safety. Full article